Sponsorship activation.

 

 

You may attend a live sporting event or watch any event (2-hour minimum) on television. As
you watch, pay close attention to all the sponsorship activation. This can be commercials, event
signage, in-game features, sponsored broadcast segments, announcer mentions, or related
social or digital promotions such as contests, hashtags, or other participatory activations.
While you watch the sporting event, fill out the sponsor activation matrix provided.
Once the matrix is full, evaluate what you saw, and think about the level of effectiveness of each
of the sponsor messages.
• What stood out to you?
• What was barely noticeable?
• Was anything creative or unique?
• Was there a lot of sponsorship clutter or were the sponsors easily recognizable?
• Was there category exclusivity or overlap in the brands activating?
• Do you think the activations had an impact?

Sample Solution

ational public perception and policy agendas (Gilliam Jr and Iyengar 2000, Walgrave et al. 2008, Baumgartner and Jones 2010). Media coverage is generally thought to influence policy agendas in two primary ways. First, media coverage can influence the relative salience (importance or prominence) of a particular pubic issue through repeated coverage over time (Soroka 2003, Baumgartner and Jones 2010). Second, media coverage can influence public and policy conceptualization about an issue and coalescence – how an issue is understood, defined, and framed (Elder and Cobb 1983). This conceptualization of an issue can influence the perception of the possible solutions and the importance of addressing the problem with governmental policy (Weart 1988, Baumgartner and Jones 2010). However, there is mixed evidence for how these factors – problem severity, interest groups, media coverage, and public perception – may act together to influence policy generation (McCombs and Shaw 1972, Funkhouser and Shaw 1990, Entman 1993, Koch-Baumgarten and Voltmer 2010).

Our objectives were to characterize the relative influence of the factors that led to the establishment of the APHIS National Feral Swine Damage Management Program in 2013 (federal government fiscal year 2014). Specifically we wanted to understand 1) the significance of public policy image on congressional policy activity; 2) to assess the influence of problem severity and broad governmental institutional pressures associated with expansion of wild swine at a national level on policy activity; and 3) to identify predictors of policy activity for informing wildlife and agricultural interface management; specifically program assessments and new program development. Here we use the term ‘policy’ in its broadest definition referring not only to operational policies of government but also including all dialogue related to the development of policy. To investigate the relationship between policies, wild swine, and agriculture we use 29 years of data from three primary datasets – number of wild swine related policy actions (response variable), newspaper headline data, and the amount of agriculture in wild swine regions. Based on studies suggesting a strong dependence of policy change on changes in public policy image (Jones and Baumgartner 2004, Baumgartner and Jones 2010), specifically increased policy activity when public policy images become negative, we hypothesized that significant increase in the number of negative newspaper articles would act as a mechanism for influencing policy activity and provide a link between changes in policy and expanding wild swine populations. Because governmental institutions tend to increase stability in policy areas (Jones et al. 2003, Baumgartner and Jones 2010), we hypothesized that increasing the amount of agriculture in wild swine regions might be related to increasing problem severity and result in increased pressures on federal governmental institutions. Thus increasing policy activity as agricultural related interests increased demands for policy solutions to wild swine related issues – agricultural damage and economic losses. In our statistical models, we wanted to estimate these effects and determine if these patterns are consistent with increased policy activity. The broader goal of this analysis is to provide a mechanistic understanding of the policy image and institutional conditions that give rise to variations in the policy process, which enables improved response to changes in conditions that impact both wildlife and agricultural policy.

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